CXCL13 expression in mouse 4T1 breast cancer microenvironment elicits antitumor immune response by regulating immune cell infiltration

Rapid qPCR-based quantitative immune cell phenotyping in mouse tissues

The immune microenvironment plays an important role in the regulation of diseases. The characterization of the cellular composition of immune cell infiltrates in diseases and respective models is a major task in pathogenesis research and diagnostics. For the assessment of immune cell populations in tissues, fluorescence-activated cell sorting (FACS) or immunohistochemistry (IHC) are the two most common techniques presently applied, but they are cost intensive, laborious, and sometimes limited by the availability of suitable antibodies. Complementary rapid qPCR-based approaches exist for the human situation but are lacking for experimental mouse models. Accordingly, we developed a robust, rapid RT-qPCR-based approach to determine and quantify the abundance of prominent immune cell populations such as T cells, helper T (Th) cells, cytotoxic T cells, Th1 cells, B cells, and macrophages in mouse tissues. The results were independently validated by the gold standards IHC and FACS in corresponding tissues and showed high concordance.

Natural products derived from medicinal plants and microbes might act as a game-changer in breast cancer: a comprehensive review of preclinical and clinical studies

Breast cancer (BC) is the most prevalent neoplasm among women. Genetic and environmental factors lead to BC development and on this basis, several preventive - screening and therapeutic interventions have been developed. Hormones, both in the form of endogenous hormonal signaling or hormonal contraceptives, play an important role in BC pathogenesis and progression. On top of these, breast microbiota includes both species with an immunomodulatory activity enhancing the host's response against cancer cells and species producing proinflammatory cytokines associated with BC development. Identification of novel multitargeted therapeutic agents with poly-pharmacological potential is a dire need to combat advanced and metastatic BC. A growing body of research has emphasized the potential of natural compounds derived from medicinal plants and microbial species as complementary BC treatment regimens, including dietary supplements and probiotics. In particular, extracts from plants such as Artemisia monosperma Delile, Origanum dayi Post, Urtica membranacea Poir. ex Savigny, Krameria lappacea (Dombey) Burdet & B.B. Simpson and metabolites extracted from microbes such as Deinococcus radiodurans and Streptomycetes strains as well as probiotics like Bacillus coagulans and Lactobacillus brevis MK05 have exhibited antitumor effects in the form of antiproliferative and cytotoxic activity, increase in tumors' chemosensitivity, antioxidant activity and modulation of BC - associated molecular pathways. Further, bioactive compounds like 3,3'-diindolylmethane, epigallocatechin gallate, genistein, rutin, resveratrol, lycopene, sulforaphane, silibinin, rosmarinic acid, and shikonin are of special interest for the researchers and clinicians because these natural agents have multimodal action and act via multiple ways in managing the BC and most of these agents are regularly available in our food and fruit diets. Evidence from clinical trials suggests that such products had major potential in enhancing the effectiveness of conventional antitumor agents and decreasing their side effects. We here provide a comprehensive review of the therapeutic effects and mechanistic underpinnings of medicinal plants and microbial metabolites in BC management. The future perspectives on the translation of these findings to the personalized treatment of BC are provided and discussed.

Taxifolin Inhibits Breast Cancer Growth by Facilitating CD8+ T Cell Infiltration and Inducing a Novel Set of Genes including Potential Tumor Suppressor Genes in 1q21.3

Taxifolin inhibits breast cancer (BC) via novel mechanisms. In a syngeneic mouse BC model, taxifolin suppressed 4T-1 cell-derived allografts. RNA-seq of 4T-1 tumors identified 36 differentially expressed genes (DEGs) upregulated by taxifolin. Among their human homologues, 19, 7, and 2 genes were downregulated in BCs, high-proliferative BCs, and BCs with high-fatality risks, respectively. Three genes were established as tumor suppressors and eight were novel to BC, including HNRN, KPRP, CRCT1, and FLG2. These four genes exhibit tumor suppressive actions and reside in 1q21.3, a locus amplified in 70% recurrent BCs, revealing a unique vulnerability of primary and recurrent BCs with 1q21.3 amplification with respect to taxifolin. Furthermore, the 36 DEGs formed a multiple gene panel (DEG36) that effectively stratified the fatality risk in luminal, HER2+, and triple-negative (TN) equivalent BCs in two large cohorts: the METABRIC and TCGA datasets. 4T-1 cells model human TNBC cells. The DEG36 most robustly predicted the poor prognosis of TNBCs and associated it with the infiltration of CD8+ T, NK, macrophages, and Th2 cells. Of note, taxifolin increased the CD8+ T cell content in 4T-1 tumors. The DEG36 is a novel and effective prognostic biomarker of BCs, particularly TNBCs, and can be used to assess the BC-associated immunosuppressive microenvironment.

Establishment of a novel cytokine-related 8-gene signature for distinguishing and predicting the prognosis of triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is a common carcinoma in women, and the prognosis of TNBC is the worst. Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the functional roles of cytokine-related genes in TNBC.

Methods

The clinical and transcriptome data of TNBC patients were downloaded from TCGA database. A systematical analyses of the data from TCGA database were conducted to screen the prognostic genes and identify the main cytokine-related pathways related to TNBC.

Results

We identified 499 prognostic genes in TNBC patients from TCGA database and the cytokine-related pathways closely related to TNBC. TCGA-TNBC patients were divided into the high-risk cluster (C1) group and the low-risk cluster (C2) group based on the cytokine-related genes. The C1 group patients exhibited tumor metastasis and an advanced tumor stage. The functional analysis revealed that the upregulated differentially expressed genes (DEGs) in the C1 group were mainly associated with the extracellular matrix (ECM)-receptor interaction, stem cell proliferation, focal adhesion, and cyclic adenosine monophosphate (cAMP) signaling pathway, while the downregulated DEGs in the C1 group were mainly associated with cytokine and cytokine receptors, T-helper 17 (Th17) cell differentiation, and primary immunodeficiency. The immune activity of C1 group was lower than that of C2 group, and the identified half-maximal inhibitory concentration scores of 3 chemotherapy drugs (i.e., doxorubicin, methotrexate, and paclitaxel) were lower in C2 group than C1 group. More importantly, we constructed a novel prognostic signature and identified the following 8 genes: CCL25, CXCL13, IL12RB2, IL21, TNFRSF13C, TNFRSF8, CCL7 and GDF5.

Conclusion

The status of the cytokine-related pathway was closely related to tumor classification and immune activity in the TNBC patients. The gene signature of the cytokine-related genes showed an good performance in predicting the prognosis of TNBC patients, and could predict the prognosis of TNBC patients.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

Machine learning predicts cancer subtypes and progression from blood immune signatures

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual’s immune landscape may play an important role in treatment personalisation. Peripheral blood is a readily accessible source of information to study an individual’s immune landscape compared to more complex and invasive tumour bioipsies, and may hold immense diagnostic and prognostic potential. Identifying the critical components of these immune signatures in peripheral blood presents an attractive alternative to tumour biopsy-based immune phenotyping strategies. We used two syngeneic solid tumour models, a 4T1 breast cancer model and a CT26 colorectal cancer model, in a longitudinal study of the peripheral blood immune landscape. Our strategy combined two highly accessible approaches, blood leukocyte immune phenotyping and plasma soluble immune factor characterisation, to identify distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were associated with 4T1 growth, whereas CCL17, CXCL10, total myeloid cells, CCL2, IL-10, CXCL1, and Ly6C^intermediate monocytes were associated with CT26 tumour development. Peripheral blood appears to be an accessible means to interrogate tumour-dependent changes to the host immune landscape, and to identify blood immune phenotypes for future treatment stratification.